NVIDIA NIM Inference Service Integration Guide

NVIDIA NIM Introduction

NVIDIA NIM (NVIDIA Inference Microservices) packages large language models into Docker containers optimized for NVIDIA GPUs. NIM provides an OpenAI-compatible API that integrates seamlessly with OpenClaw.

Prerequisites

NVIDIA GPU (A100/H100/L40S or consumer RTX 4090 recommended)
NVIDIA Driver 535+
Docker and NVIDIA Container Toolkit
NVIDIA NGC API Key

Deploy a NIM Container

export NGC_API_KEY="your-ngc-api-key"

docker run -d \
  --name nim-llama \
  --gpus all \
  -p 8000:8000 \
  -e NGC_API_KEY=$NGC_API_KEY \
  nvcr.io/nim/meta/llama-3.1-8b-instruct:latest

Configure in OpenClaw

{
  "providers": {
    "nvidia-nim": {
      "type": "openai",
      "baseUrl": "http://localhost:8000/v1",
      "apiKey": "not-used",
      "models": ["meta/llama-3.1-8b-instruct"]
    }
  }
}

Local NIM deployments don't require an API key, but the field must be present.

Using NVIDIA API Catalog (Cloud)

{
  "providers": {
    "nvidia-cloud": {
      "type": "openai",
      "baseUrl": "https://integrate.api.nvidia.com/v1",
      "apiKey": "{{NGC_API_KEY}}",
      "models": ["meta/llama-3.1-405b-instruct", "meta/llama-3.1-70b-instruct"]
    }
  }
}

Multi-GPU and Performance Optimization

Use tensor parallelism for large models and tune batch size, sequence length, and GPU memory utilization for optimal throughput.

Summary

NVIDIA NIM is the best choice for peak inference performance. For teams with GPU resources, local NIM deployment delivers low-latency, high-throughput inference that pairs perfectly with OpenClaw for high-performance AI assistants.